Melanoma Detection Using a Deep Learning Approach

Manzoor, S.; Qayyum, Huma; Hassan, Farman; Ullah, Asad; Nawaz, Ali; Rahman, Auliya Ur

doi:10.33411/ijist/2022040117

Cited by 7 publications

(6 citation statements)

References 20 publications

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“…A cold sore [49] is a collection of small, painful blisters caused by the herpes simplex virus (HSV). Fever blisters were commonly referred as herpes simplex labialis.…”

Section: Dermatological Conditionsmentioning

confidence: 99%

Deep Learning-Based Dermatological Condition Detection: A Systematic Review With Recent Methods, Datasets, Challenges, and Future Directions

Noronha,

Mehta,

Garg

et al. 2023

IEEE Access

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Dermatological conditions are a global health concern affecting all age groups. They encompass various skin issues such as rashes, moles, acne, blisters, hives, nodules, cysts, macules, and papules. Early diagnosis of dermatological conditions is crucial to prevent skin damage and chronic diseases. Recent advancements in artificial intelligence and medical image processing, particularly through deep learning, have significantly improved the precision and efficiency of dermatological disease detection by dermatologists. This paper thoroughly examines deep learning-based methods for detecting dermatological conditions from dermoscopic images. Specifically, it presents study of 22 methods that are used to detect dermatological conditions such as basal cell carcinoma, melanocytic nevus, seborrheic keratosis, psoriasis, benign keratosis, melanoma, acne, cold sore, warts, eczema, hives, shingles, scar, skin tag, inflammatory hyper pigment, cyst, dark circle, blackhead, burn and skin rash. It also covers clinical diagnostic methods for dermatological conditions, and the need for computer-aided diagnosis. In this paper, we have also proposed the categorization of deep learning-based dermatological condition detection methods. Moreover, the comprehensive summary of these methods is presented. In addition, this paper summarizes the majority of the datasets available for computer-aided detection of dermatological conditions. Furthermore, it presents enormous challenges and potential future research directions. This survey informs researchers about the latest advancements in deep learning-based detection methods for dermatological conditions and allows dermatologists to stay updated on technological breakthroughs in this area.

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“…A cold sore [49] is a collection of small, painful blisters caused by the herpes simplex virus (HSV). Fever blisters were commonly referred as herpes simplex labialis.…”

Section: Dermatological Conditionsmentioning

confidence: 99%

Deep Learning-Based Dermatological Condition Detection: A Systematic Review With Recent Methods, Datasets, Challenges, and Future Directions

Noronha,

Mehta,

Garg

et al. 2023

IEEE Access

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“…Continuing into [31], the ISIC 2018 database was leveraged to train and test the proposed method. Pre-processing, subsampling, and down-sampling steps were conducted to balance data, and VGG16 and ResNet-50 CNNs were used to detect melanoma.…”

Section: Related Workmentioning

confidence: 99%

Performance Improvement of Melanoma Detection Using a Multi-Network System Based on Decision Fusion

El-khatib,

Ștefan,

Popescu

2023

Applied Sciences

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The incidence of melanoma cases continues to rise, underscoring the critical need for early detection and treatment. Recent studies highlight the significance of deep learning in melanoma detection, leading to improved accuracy. The field of computer-assisted detection is extensively explored along all lines, especially in the medical industry, as the benefit in this field is to save hu-man lives. In this domain, this direction must be maximally exploited and introduced into routine controls to improve patient prognosis, disease prevention, reduce treatment costs, improve population management, and improve patient empowerment. All these new aspects were taken into consideration to implement an EHR system with an automated melanoma detection system. The first step, as presented in this paper, is to build a system based on the fusion of decisions from multiple neural networks, such as DarkNet-53, DenseNet-201, GoogLeNet, Inception-V3, InceptionResNet-V2, ResNet-50, ResNet-101, and compare this classifier with four other applications: Google Teachable Machine, Microsoft Azure Machine Learning, Google Vertex AI, and SalesForce Einstein Vision based on the F1 score for further integration into an EHR platform. We trained all models on two databases, ISIC 2020 and DermIS, to also test their adaptability to a wide range of images. Comparisons with state-of-the-art research and existing applications confirm the promising performance of the proposed system.

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“…In our case, Decision Tree (DT) has shown much better results than the Support Vector Machine and Naïve Bayes with higher accuracy that is around 99.67%. Various studies on machine learning have been conducted that show the emergence of ML in daily dynamics [52,53,54,55,56,57,58,59].…”

Section: 𝑇𝑃 𝑇𝑃 + 𝐹𝑃mentioning

confidence: 99%

Analyzing ML-Based IDS over Real-Traffic

Siyyal

Khuawar

Saba

et al. 2022

IJIST

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The rapid growth of computer networks has caused a significant increase in malicious traffic, promoting the use of Intrusion Detection Systems (IDSs) to protect against this ever-growing attack traffic. A great number of IDS have been developed with some sort of weaknesses and strengths. Most of the development and research of IDS is purely based on simulated and non-updated datasets due to the unavailability of real datasets, for instance, KDD '99, and CIC-IDS-18 which are widely used datasets by researchers are not sufficient to represent real-traffic scenarios. Moreover, these one-time generated static datasets cannot survive the rapid changes in network patterns. To overcome these problems, we have proposed a framework to generate a full feature, unbiased, real-traffic-based, updated custom dataset to deal with the limitations of existing datasets. In this paper, the complete methodology of network testbed, data acquisition and attack scenarios are discussed. The generated dataset contains more than 70 features and covers different types of attacks, namely DoS, DDoS, Portscan, Brute-Force and Web attacks. Later, the custom-generated dataset is compared to various available datasets based on seven different factors, such as updates, practical-to-generate, realness, attack diversity, flexibility, availability, and interoperability. Additionally, we have trained different ML-based classifiers on our custom-generated dataset and then tested/analyzed it based on performance metrics. The generated dataset is publicly available and accessible by all users. Moreover, the following research is anticipated to allow researchers to develop effective IDSs and real traffic-based updated datasets.

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Melanoma Detection Using a Deep Learning Approach

Cited by 7 publications

References 20 publications

Deep Learning-Based Dermatological Condition Detection: A Systematic Review With Recent Methods, Datasets, Challenges, and Future Directions

Deep Learning-Based Dermatological Condition Detection: A Systematic Review With Recent Methods, Datasets, Challenges, and Future Directions

Performance Improvement of Melanoma Detection Using a Multi-Network System Based on Decision Fusion

Analyzing ML-Based IDS over Real-Traffic

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